Fearful Symmetry: Can We Solve Ideal Lattice Problems Efficiently? Craig Gentry IBM T.J. Watson...

Fearful Symmetry:Can We Solve Ideal Lattice Problems

Efficiently?

Craig GentryIBM T.J. Watson

Workshop on Lattices with Symmetry

Can we efficiently break lattices with certain types of symmetry?

If a lattice has an orthonormal basis,

can we find it?

Can we break “ideal lattices” – lattices for ideals in number

fields – by combining geometry with algebra?

Gentry-Szydlo Algorithm

Combines geometric and algebraic techniquesto break some lattices with symmetry.

Suppose L is a “circulant” lattice with a circulant basis B.

Given any basis of L:• If B’s vectors are orthogonal, we can find B in poly time!• If we are given precise info about B’s “shape” (but not its

“orientation”) we can find B in poly time.

Gentry-Szydlo Algorithm

Combines geometric and algebraic techniquesto break some lattices with symmetry.

Suppose I = (v) is a principal ideal in a cyclotomic field.

Given any basis of the ideal lattice associated to I:• If v times its conjugate is 1, we can find v in poly time!• Given v times its conjugate, we can find v in poly time.

Overview

• Cryptanalysis of early version of NTRUSign– Some failed attempts– GS attack, including the “GS algorithm”

• Thoughts on extensions/applications of GS

Early version of NTRUSign

• Uses polynomial rings R = Z[x]/(xn-1) and Rq.

• Signatures have the form v · yi Rq.– v is the secret key– yi is correlated to the message being signed, but

statistically it behaves “randomly”– v and the yi’s are “small”: Coefficients << q

• We wanted to recover v…

How to Attack it?

• We found a way to “lift” the signatures– We obtained v · yi R “unreduced” mod q

• Now what? Some possible directions:– Geometric approach: Set up a lattice in which v is the

shortest vector?– Algebraic approach: Take the “GCD” of {v · yi} to get v?– Something else?

Adventures in Cryptanalysis:A Standard Lattice Attack

Lattices

Lattice: a discrete additive subgroup of Rn

Lattices

Basis of lattice: a set of linearly independent vectors that generate the lattice

b1

b2

Lattices

b1

b2


Lattices


b1b2

Lattices


b1b2

Different bases → same parallelepiped volume (determinant)

Lattices

b1b2


Different bases → same parallelepiped volume (determinant)

Hard Problems on Lattices

b1b2

Given “bad” basis B of L:


b1b2

Shortest vector problem (SVP):Find the shortest nonzero vector in L



b1b2

Shortest independent vector problem (SIVP):Find the shortest set of n linearly independent vectors



b1b2

Closest vector problem (CVP):Find the closest L-vector to v

v



b1b2

Bounded distance decoding (BDDP):Output closest L-vector to v, given that it is very close

v



b1b2

γ-Approximate SVPFind a vector at most γ times as long as the shortest nonzero vector in L


Canonical Bad Basis: Hermite Normal Form

Every lattice L has a canonical basis B = HNF(L). Some properties:• Upper triangular• Diagonal entries Bi,i are positive

• For j < i, Bj,i < Bi,i (entries of above the diagonal are smaller)• Compact representation: HNF(L) expressible in O(n log d) bits,

where d is the absolute value of the determinant of (any) basis of L.• Efficiently computable: from any other basis, using techniques

similar to Gaussian elimination.• The “baddest basis”: HNF(L) “reveals no more” about structure of L

than any other basis.

Lattice Reduction Algorithms

Given a basis B of an n-dimensional lattice L:• LLL (Lenstra Lenstra Lovász ‘82): outputs v L with

v< 2n/2·λ1(L) in poly time.• Kannan/Micciancio: outputs shortest vector in

roughly 2n time.• Schnorr: outputs v L with v< kO(n/k)·λ1(L) in time

kO(k).

• No algorithm is both very fast and very effective.

Back to Our Cryptanalysis…

• Goal: Get v from v · yi R = Z[x]/(xn-1) by making v be a short vector in some lattice.

• Why it seems hopeless:– v is a short vector in a certain n-dimensional lattice– But n is big! Too big for efficient lattice reduction.

• Let’s go over the approach anyway…

Lattice of Multiples of v(x)

• Let L = lattice generated by our v(x)·yi(x) sigs.– L likely contains all multiples of v(x).– If so, v(x) is a short(est) vector in L.

• Can we reduce L? What is L’s dimension? Does it have structure we can exploit?

Ideal Lattices• Definition of an ideal of a ring R

– I is a subset of R– I is additively closed (basically, a lattice)– I is closed under multiplication with elements of R

(3) = polynomials in R that are divisible by 3

(v(x)) = multiples of v(x) R:{ v(x)r(x) mod f(x) : r(x) R }.

• Ideal lattice: a representation of an ideal as a free Z-module (a lattice) of rank n generated by some n-dimensional basis B.

Circulant Lattices and Polynomials

Computing B·w is like computing v(x)·w(x)

Rotation basis of v(x) generates

ideal lattice I = (v)

Why Lattice Reduction Fails Here

• v’s ideal lattice has dimension n.• The lattice has lots of structure

– An underlying circulant “rotation” basis– But lattice reduction algorithms don’t exploit it.

Adventures in Cryptanalysis:An Algebraic Failure

Why Can’t We Take the GCD?

• Given v · yi R = Z[x]/(xn-1), why can’t we take the GCD, like we could over Z?

• In Z, the only units are {-1,1}.• In R, there are infinitely many units.

– Example of a “nontorsion” unit: (1-xk)/(1-x) for any k relatively prime to n.

• v is not uniquely defined by {v · yi} if one ignores the smallness condition!• Must incorporate geometry somehow…

Adventures in Cryptanalysis:Let’s get to the successes…

Gentry-Szydlo Attack

• Step 1: Lift sigs to get {v·yi}.• Step 2: Averaging attack to obtain where (x) =

v(x-1) mod xn-1. (Hoffstein-Kaliski)• Step 3: Recover v from and a basis of the ideal

lattice I = (v).

What is this thing

• (x) = v(x-1) = v0 + vn-1x +…+ v1xn-1

– The “reversal” of v.• (x)’s rotation basis is the transpose of v(x)’s:

: A Geometric Goldmine

• So, contains all the mutual dot products in v’s rotation basis– A lot of geometric information about v.

• ’s rotation basis is B·BT, the Gram matrix of B!

: Important Algebraically Too

• The R-automorphism x → x-1 sends to itself.• Algebraic context: We have really been working in the field

K=Q() where is a n-th root of unity.• K is isomorphic to Z[x]/(n(x)), where n(x) is the n-th

cyclotomic polynomial.– Very similar to the NTRUSign setting

• K has (n) embeddings into C, given by σi()→ for gcd(i,n)=1.

• The value σ1(v)·σ-1(v) = is the relative norm NmK/K+(v) of v wrt the index 2 real subfield K+ = Q().

Averaging Attack

Consider the average:

The 0-th coefficient of is very big – namely 2.

The others are smaller, “random”, and possibly negative, and so averaging cancels them out.

So, converges to some known constant c, and to .

Averaging Attack

The imprecision of the average is proportional to .

Since has small (poly size) coefficients, only a poly number of sigs are needed to recover by rounding.

Finally, the “Gentry-Szydlo Algorithm”

Overview of the GS Algorithm

• Goal: Recover v from and a basis of the ideal lattice I = (v).

• Strategy (a first approximation): – Pick a prime P > 2n/2 with P = 1 mod n.– Compute basis of ideal IP-1.– Reduce it using LLL to get vP-1·w, where |w| < 2n/2.– By Fermat’s Little Theorem, vP-1 = 1 mod P, and so

we can recover w exactly, hence vP-1 exactly.– From vP-1, recover v.

GS Overview: Issue 1

• Issue 1: How do we guarantee w is small?– LLL only guarantees a bound on vP-1·w.– v could be skewed by units, and therefore so can w.

• Solution 1 (Implicit Lattice Reduction): – Apply LLL implicitly to the multiplicands of vP-1.– The value allows us to “cancel” v’s geometry so that

LLL can focus on the multiplicands only.– (I’ll talk more about this in a moment)

GS Overview: Issue 2

• Issue 2: LLL needs P to be exponential in n.– But then IP-1 and vP-1 take an exponential number

of bits to write down.

• Solution 2 (Polynomial Chains):– Mike will go over this, but here is a sketch…

Polynomial Chains (Sketch)

• We do use P > 2n/2, but compute vP-1 implicitly.• vP-1 and w are represented by a chain of unreduced

smallish polynomials that are computed using LLL. • From the chain, we get w ← (vP-1·w mod P) unreduced.• After getting w exactly, we reduce it mod some small

primes p1,…, pt, and get vP-1 mod these primes.• Repeat for prime P’ > 2n/2 where gcd(P-1,P’-1) = 2n.• Compute v2n = vgcd(P-1,P’-1) mod the small primes.• Use CRT to recover v2n exactly.• Finally, recover v.

Conceptual Relationship with “Coppersmith’s Method”

• Find small solutions to f(x) = 0 mod N– Construct lattice of polynomials gi(x) = 0 mod N.– LLL-reduce to obtain h(x) = 0 mod N for small h.– h(x) = 0 mod N → h(x) = 0 (unreduced)– Solve for x.

• GS Algorithm– Obtain vP-1·w for small w.– vP-1·w = [z] mod P → w = [z] (unreduced)

Implicit Lattice Reduction

• Claim: For v R, given and HNF((v)), we can efficiently output u = v·a such that |a| < 2n/2.

• LLL only needs Gram matrix BT· B when deciding to swap or size-reduce its basis-so-far B.

• Same is true of ideal lattices: only needs { }.• Compute { } from { } and ()-1.• Apply LLL directly to the ’s.

A Possible Simplication of GS?

Can We Avoid Polynomial Chains?

• If vr = 1 mod Q for small r and composite Q > 2n/2, maybe it still works and we can write vr down.

• Set r = n·Πpi, where pi runs over first k primes.– Suppose k = O(log n).

• Set Q = ΠP such P-1 divides r. Note: vr = 1 mod Q.


• Now what is the size of Q?• Let T = {1+n· : subset S of [k]}• Let Tprime = prime numbers in T.


• Answer: not quite.• r is quasi-polynomial.• So, the algorithm is quasi-polynomial.

• We can extend the above approach to handle (1+1/r)-approximations of .

GS Makes Principal Ideal Lattices Weak

Dimension-Halving in Principal Ideal Lattices

• For any n-dim principal ideal lattice I = (v):

Solving 2-approximate SVP in I< Solving SVP in some n/2-dim lattice.

• “Breaking” principal ideal lattices seems easier than breaking general ideal lattices.

• Attack uses GS algorithm

• A

Dimension-Halving in Principal Ideal Lattices

• Given I = (v), generate a basis B2 of (u) for u=v/.• Use GS to obtain u.

– Note: We already have = 1.• From 1+ 1/() = (v+)/v and I, generate a basis B3 of

(v+).• Note: v+ is in index-2 real subfield K+ = Q(ζ+ζ-1).• Project basis B3 down K+ to get basis B4 of

elements (v+)·r with r in K+.• Multiply elements in B4 by v/(v+) to get lattice L4

of elements v·r with r in K+.• Claim: λ1(L4) ≤ 2λ1((v)).

Thanks! Questions?

??TIME

EXPIRED

Averaging Attack

Ideal Lattices• Definition of an ideal:


• Product: I J = additive closure of {i j : i I, j J}∙ ∙



Ideal Lattices• Definition of an ideal:




Ideal Lattice

• Ideal lattice: a representation of an ideal as a free Z-module (a lattice) of rank n generated by some n-dimensional basis B.

Principal Ideal Generator Problem

• PIG Problem: Given an ideal lattice L of a principal ideal I, output v such that I = (v).

Ideals in Polynomial Rings

• Inverse of an Ideal– Definition: Let K = Q(x)/f(x) be the overlying field.

Then, I-1 = {v K : for all i I, v i R}∙– E.g. (3)-1 = (1/3).– Principal ideals: (v)-1 = (1/v)– Non-principal: more complicated, but they still

have inverses

Ideals Are Like Integers

• Norm: Nm(I) = |R/I| = determinant of basis of I– Norm map is multiplicative: Nm(I∙J) = Nm(I)∙Nm(J)

• Primality: I is prime if I dividing JK implies I divides J or I divides K– Prime ideals have norm that is a prime power

• Unique factorization: Each ideal I of R = Z[x]/(xn+1)) factors uniquely into prime ideals

• Prime Ideal Theorem (cf. Prime Number Th.):– # of prime ideals with norm ≤ x is close to x/ln(x)

Ideals Are Like Integers

• Factoring ideals reduces to factoring integers – Kummer-Dedekind:

• Consider the factorization of f(x) = ∏i gi(x) mod p.

• In Z[x]/f(x), the prime ideal factors pi whose norm are a power of p are precisely: pi = (p, gi(x))

– Polynomial factorization mod p• Is efficient (e.g., Kaltofen-Shoup algorithm)

– Bottom line: We can factor I if we can factor Nm(I)

Dimension-Halving Attack on Circulant Bases

More Algebra

Why lattices are cool for crypto/ Context

• No quantum attacks on lattices– in contrast to RSA, elliptic curves, …

• Worst-case / average-case connection– Ajtai (‘96): solving average instances of some lattice problem

implies solving worst-case instances of some lattice problem

Dimension-Halving for Principal Ideal Lattices

• [GS’02]: Given – a basis of I = (u) for u(x) 2 R and– u’s relative norm u(x)ū(x) in the index-2 subfield

Q(ζN+ ζN-1),

we can compute u(x) in poly-time.

• Corollary: Set v(x) = u(x)/ū(x). We can compute v(x) given a basis of J = (v). – We know v(x)’s relative norm equal 1.

Dimension-Halving for Principal Ideal Lattices

• Attack given a basis of I = (u):– First, compute v(x) = u(x)/ū(x).– Given a basis {u(x)ri(x)} of I, multiply by 1+1/v(x) to

get a basis {(u(x)+ ū(x))ri(x)} of K = (u(x)+ū(x)) over R.

– Intersect K’s lattice with subring R’ = Z[ζN+ ζN-1] to get

a basis {(u(x)+ ū(x))si(x) : si(x) 2 R’} of K over R’.

– Apply lattice reduction to lattice {u(x)si(x) : si(x) 2 R’}, which has half the usual dimension.

Before Step 3:An Geometric Interlude

(Implicit Lattice Reduction)

Implicit Lattice Reduction

Before Step 3:An Algebraic Interlude

Fearful Symmetry: Can We Solve Ideal Lattice Problems Efficiently? Craig Gentry IBM T.J. Watson...

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